Stand Up & Wheel Count Size Location

Team Stand-Up

We meet today original for a team coding scrimmage, in order to get a chunk of the coding work out of the way. However, due to time constraints everyone had with regards to other module work & assignments. In place of this, we had a quick Stand-Up meeting, whereby we discussed our current work thus far, as well as our plans for moving forward and any possible barriers stopping us completing these tasks.

Full minutes can be found here. (DIT Sign-in required)

Wheel Detection

# Original Code
circles = cv2.HoughCircles(img, #src Image
cv2.HOUGH_GRADIENT, # Hough Circling Method
1, # DP
30, # Minimum distance between centroids
param1=140, # Canny Edge Detection Thresholding
param2=40, # Accumulator threshold for the circle centers. Lower means more false positives
minRadius=0, # Minimum radius allowed for a detected Circle
maxRadius=50) # Maximum radius allowed for a detected Circle

Testing the code

The above code was used to test it's results at capturing all the wheels in the project vehicle images. I had various results, and had to tweak the HoughCircles parameters according to the best fit for each vehicle. So by accumulating the best cases for each vehicle, I came up with a compromising set of parameters used below.

cols = ['Wheel No.', 'X Coords', 'Y Coords','Wheel Diam']
circleLocations = []

# Working best if we take the greyscaled image as HoughCircles fxn preforms canny edge on the image anyway
img = cv2.medianBlur(greyscale_image,5)
cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)
circles = cv2.HoughCircles( img,
                            cv2.HOUGH_GRADIENT,
                            1,
                            30,
                            param1=160,
                            param2=25,
                            minRadius=10,
                            maxRadius=50)

circles = np.uint16(np.around(circles))
for index,i in enumerate(circles[0,:]):
    # draw the outer circle
    cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2)
    # draw the center of the circle
    cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)
    # append to list that'll be added to the dataframe
    circleLocations.append([index+1, i[0], i[1], i[2]])

wheelData = pd.DataFrame(circleLocations, columns=cols)
plt.imshow(cimg)
print(wheelData)

# TODO There could be a point that if the circle's y coord is along the same plane, so a buffer for +- 30 pixels,
# then only include those circles

    Wheel No.  X Coords  Y Coords  Wheel Diam
0           1       488       108          44
1           2       524        78          28
2           3       558       108          29
3           4       600       188          30
4           5       634        90          49
5           6        72        90          37
6           7       668       186          30
7           8       424        48          48
8           9       226       196          13
9          10       598       118          29
10         11       362        68          29
11         12       246        78          21
12         13       528       186          29
13         14       572        64          30
14         15       300        82          28
15         16       170       196          12
16         17        62       130          17
17         18        66       196          11
18         19        90       144          22
19         20       438        76          17

actualWheels = []
biggerWheel = 0
# Using just the lower third of the image to find the wheels
lowerThirdBuffer = image.shape[0] * 0.66

for index,row in enumerate(wheelData.iterrows()):
    # Let's find the largest wheel, and take that as the vehicle's wheel size
    if(row[1][3] > biggerWheel):
        biggerWheel = row[1][3]
    # Appends the actual wheels to a new list
    if(row[1][2] > lowerThirdBuffer):
        row[1][3] = biggerWheel
        actualWheels.append(list(row[1]))

actualWheels = pd.DataFrame(actualWheels,columns=cols)
print(actualWheels)

   Wheel No.  X Coords  Y Coords  Wheel Diam
0          4       600       188          44
1          7       668       186          49
2          9       226       196          49
3         13       528       186          49
4         16       170       196          49
5         18        66       196          49